Ensuring grounded data only ever includes what the requesting person or system is authorised to see, in a form that respects field-level sensitivity, and never…
Formatting retrieved, resolved data into a form an LLM can actually use — structured blocks, reusable snippets, task-specific bundles — and letting the…
Calling the right systems, and correctly matching the same real-world entity — an account, a case, an employee — across systems that disagree on identifiers,…
Turning a business user's natural-language ask, or a program's structured request, into a resolved statement of what enterprise data is actually needed — which…
Recording what data, from which system, grounded which prompt, for whom, and making that record usable — for usage analytics, for anomaly detection, and for…
Classifying a grounded prompt by sensitivity and provenance, and routing it down the execution path that classification earns — immediate execution, logged…
Treating a prompt template as a governed artefact with an owner, a version, and a lifecycle — authored, reviewed, published, eventually retired — rather than a…